Add to ORKGA promising approach to solving large state-space search problems is to integrate heuristic search with symbolic search. Recent work shows that a symbolic A * search algorithm that uses binary decision diagrams to compactly represent sets of states outperforms traditional A * in many domains. Since the memory requirements of A * limit its scalability, we show how to integrate symbolic search with a memory-efficient strategy for heuristic search. We analyze the resulting search algorithm, consider the factors that affect its behavior, and evaluate its performance in solving benchmark problems that include STRIPS planning problems
We describe a planning algorithm that integrates two approaches to solving Markov decision processes...
The objective of optimal oversubscription planning is to find a plan that yields an end state with a...
Optimal heuristic searches such as A * search are widely used for planning but can rarely scale to l...
A promising approach to solving large state-space search problems is to integrate heuristic search w...
Symbolic search, using Binary Decision Diagrams (BDDs) to represent sets of states, is a competitive...
We show how to use symbolic model-checking techniques in heuristic search algorithms for both deter...
In this paper we study traditional and enhanced BDD-based exploration procedures capable of handling...
We describe a planning algorithm that integrates two approaches to solving Markov decision processe...
In this paper we propose refinements for optimal search with symbolic pattern databases in determini...
Planning in nondeterministic domains has gained more and more importance. Conformant planning is the...
Symbolic search with BDDs has shown remarkable performance for cost-optimal deterministic planning b...
We describe a planning algorithm that integrates two ap-proaches to solving Markov decision processe...
A Reduced Ordered Binary Decision Diagram (BDD) is a symbolic data structure introduced to the model...
Search is an important topic in many areas of AI. Search problems often result in an immense number ...
Symbolic search allows saving large amounts of memory compared to regular explicit-state search algo...
We describe a planning algorithm that integrates two approaches to solving Markov decision processes...
The objective of optimal oversubscription planning is to find a plan that yields an end state with a...
Optimal heuristic searches such as A * search are widely used for planning but can rarely scale to l...
A promising approach to solving large state-space search problems is to integrate heuristic search w...
Symbolic search, using Binary Decision Diagrams (BDDs) to represent sets of states, is a competitive...
We show how to use symbolic model-checking techniques in heuristic search algorithms for both deter...
In this paper we study traditional and enhanced BDD-based exploration procedures capable of handling...
We describe a planning algorithm that integrates two approaches to solving Markov decision processe...
In this paper we propose refinements for optimal search with symbolic pattern databases in determini...
Planning in nondeterministic domains has gained more and more importance. Conformant planning is the...
Symbolic search with BDDs has shown remarkable performance for cost-optimal deterministic planning b...
We describe a planning algorithm that integrates two ap-proaches to solving Markov decision processe...
A Reduced Ordered Binary Decision Diagram (BDD) is a symbolic data structure introduced to the model...
Search is an important topic in many areas of AI. Search problems often result in an immense number ...
Symbolic search allows saving large amounts of memory compared to regular explicit-state search algo...
We describe a planning algorithm that integrates two approaches to solving Markov decision processes...
The objective of optimal oversubscription planning is to find a plan that yields an end state with a...
Optimal heuristic searches such as A * search are widely used for planning but can rarely scale to l...